Kelders S.A. (2013) LaMaLVQ: Warping Space. Master's Thesis / Essay, Computing Science.
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Abstract
LVQ is a prototype based classification technique. This technique can be extended to MLVQ to include metric learning. LMNN is a classification technique based on kNN and extended to incorporate metric learning. MLVQ and LMNN show many similarities in the way distance is defined. A combination of the techniques is therefore an enticing experiment. LaMaLVQ attempts to combine the metric learning of LMNN and the low space requirements of LVQ into one, by letting the LVQ part position the prototypes and the LaMa part compute an appropriate metric for the prototype configuration. This technique shows some differing but interesting results. For example: letting the two parts share the same optimization goal may seem like a good idea, but can have detrimental effect on classification. Also, LaMaLVQ shows a tendency to push prototypes away from the dataset.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:56 |
| Last Modified: | 15 Feb 2018 07:56 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/11518 |
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